Software Engineering for Data Scientists

Spis treści ebooka

• Preface
  • Who Is This Book For?
  • Why Python?
  • What Is Not in This Book
  • Guide to This Book
    • Reading Order
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. What Is Good Code?
  • Why Good Code Matters
  • Adapting to Changing Requirements
  • Simplicity
    • Dont Repeat Yourself (DRY)
    • Avoid Verbose Code
  • Modularity
  • Readability
    • Standards and Conventions
    • Names
    • Cleaning up
    • Documentation
  • Performance
  • Robustness
    • Errors and Logging
    • Testing
  • Key Takeaways
• 2. Analyzing Code Performance
  • Methods to Improve Performance
  • Timing Your Code
  • Profiling Your Code
    • cProfile
    • line_profiler
    • Memory Profiling with Memray
  • Time Complexity
    • How to Estimate Time Complexity
    • Big O Notation
  • Key Takeaways
• 3. Using Data Structures Effectively
  • Native Python Data Structures
    • Lists
    • Tuples
    • Dictionaries
    • Sets
  • NumPy Arrays
    • NumPy Array Functionality
    • NumPy Array Performance Considerations
    • Array Operations Using Dask
    • Arrays in Machine Learning
  • pandas DataFrames
    • DataFrame Functionality
    • DataFrame Performance Considerations
  • Key Takeaways
• 4. Object-Oriented Programming and Functional Programming
  • Object-Oriented Programming
    • Classes, Methods, and Attributes
    • Defining Your Own Classes
    • OOP Principles
      • Inheritance
      • Encapsulation
      • Abstraction
      • Polymorphism
  • Functional Programming
    • Lambda Functions and map()
    • Applying Functions to DataFrames
  • Which Paradigm Should I Use?
  • Key Takeaways
• 5. Errors, Logging, and Debugging
  • Errors in Python
    • Reading Python Error Messages
    • Handling Errors
    • Raising Errors
  • Logging
    • What to Log
    • Logging Configuration
    • How to Log
  • Debugging
    • Strategies for Debugging
    • Tools for Debugging
  • Key Takeaways
• 6. Code Formatting, Linting, and Type Checking
  • Code Formatting and Style Guides
    • PEP8
    • Import Formatting
    • Automatic Code Formatting with Black
  • Linting
    • Linting Tools
    • Linting in Your IDE
  • Type Checking
    • Type Annotations
    • Type Checking with mypy
  • Key Takeaways
• 7. Testing Your Code
  • Why You Should Write Tests
  • When to Test
  • How to Write and Run Tests
    • A Basic Test
    • Testing Unexpected Inputs
    • Running Automated Tests with Pytest
  • Types of Tests
    • Unit Tests
    • Integration Tests
  • Data Validation
    • Data Validation Examples
    • Using Pandera for Data Validation
    • Data Validation with Pydantic
  • Testing for Machine Learning
    • Testing Model Training
    • Testing Model Inference
  • Key Takeaways
• 8. Design and Refactoring
  • Project Design and Structure
    • Project Design Considerations
    • An Example Machine Learning Project
  • Code Design
    • Modular Code
    • A Code Design Framework
    • Interfaces and Contracts
    • Coupling
  • From Notebooks to Scalable Scripts
    • Why Use Scripts Instead of Notebooks?
    • Creating Scripts from Notebooks
  • Refactoring
    • Strategies for Refactoring
    • An Example Refactoring Workflow
  • Key Takeaways
• 9. Documentation
  • Documentation Within the Codebase
    • Names
    • Comments
    • Docstrings
    • Readmes, Tutorials, and Other Longer Documents
  • Documentation in Jupyter Notebooks
  • Documenting Machine Learning Experiments
  • Key Takeaways
• 10. Sharing Your Code: Version Control, Dependencies, and Packaging
  • Version Control Using Git
    • How Does Git Work?
    • Tracking Changes and Committing
    • Remote and Local
    • Branches and Pull Requests
  • Dependencies and Virtual Environments
    • Virtual Environments
    • Managing Dependencies with pip
    • Managing Dependencies with Poetry
  • Python Packaging
    • Packaging Basics
    • pyproject.toml
    • Building and Uploading Packages
  • Key Takeaways
• 11. APIs
  • Calling an API
    • HTTP Methods and Status Codes
    • Getting Data from the SDG API
  • Creating Your Own API Using FastAPI
    • Setting Up the API
    • Adding Functionality to Your API
      • Adding a GET endpoint
      • Adding a POST endpoint
    • Making Requests to Your API
  • Key Takeaways
• 12. Automation and Deployment
  • Deploying Code
  • Automation Examples
    • Pre-Commit Hooks
    • GitHub Actions
  • Cloud Deployments
    • Containers and Docker
    • Building a Docker Container
    • Deploying an API on Google Cloud
    • Deploying an API on Other Cloud Providers
  • Key Takeaways
• 13. Security
  • What Is Security?
  • Security Risks
    • Credentials, Physical Security, and Social Engineering
    • Third-Party Packages
    • The Python Pickle Module
    • Version Control Risks
    • API Security Risks
  • Security Practices
    • Security Reviews and Policies
    • Secure Coding Tools
    • Simple Code Scanning
  • Security for Machine Learning
    • Attacks on ML Systems
    • Security Practices for ML Systems
  • Key Takeaways
• 14. Working in Software
  • Development Principles and Practices
    • The Software Development Lifecycle
    • Waterfall Software Development
    • Agile Software Development
    • Agile Data Science
  • Roles in the Software Industry
    • Software Engineer
    • QA or Test Engineer
    • Data Engineer
    • Data Analyst
    • Product Manager
    • UX Researcher
    • Designer
  • Community
    • Open Source
    • Speaking at Events
    • The Python Community
  • Key Takeaways
• 15. Next Steps
  • The Future of Code
  • Your Future in Code
  • Thank You
• Index

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